Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Aykut Coskun*, Armağan Karahanoğlu

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Human-Computer Interaction (HCI) researchers have been increasingly interested in investigating self-trackers’ experience with self-tracking tools (STT) to get meaningful insights from their data. However, the literature lacks a coherent, integrated and dedicated source on designing tools that support self-trackers’ sensemaking practices. To address this, we carried out a systematic literature review by synthesizing the findings of 91 articles published before 2021 in HCI literature. We identified four data sensemaking modes that self-trackers go through (i.e., self-calibration, data augmentation, data handling, and realization). We also identified four design implications for designing self-tracking tools that support self-trackers’ data sensemaking practices (i.e., customized tracking experience, guided sensemaking, collaborative sensemaking, and learning sensemaking through self-experimentation). We provide a research agenda with nine directions for advancing HCI studies on data sensemaking practices. With these contributions, we created an analytical information source that could guide designers and researchers in understanding, studying, and designing for self-trackers’ data sensemaking practices.
Original languageEnglish
Number of pages22
JournalInternational journal of human-computer interaction
Early online date26 May 2022
DOIs
Publication statusE-pub ahead of print/First online - 26 May 2022

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